Configurations

The above are all simple text search examples. As mentioned before, full text search functionality includes the ability to do many more things: skip indexing certain words (stop words), process synonyms, and use sophisticated parsing, e.g., parse based on more than just white space. This functionality is controlled by text search configurations. PostgreSQL comes with predefined configurations for many languages, and you can easily create your own configurations. (psql's \dF command shows all available configurations.)

During installation an appropriate configuration is selected and default_text_search_config is set accordingly in postgresql.conf. If you are using the same text search configuration for the entire cluster you can use the value in postgresql.conf. To use different configurations throughout the cluster but the same configuration within any one database, use ALTER DATABASE ... SET. Otherwise, you can set default_text_search_config in each session.

Each text search function that depends on a configuration has an optional regconfig argument, so that the configuration to use can be specified explicitly. default_text_search_config is used only when this argument is omitted.

To make it easier to build custom text search configurations, a configuration is built up from simpler database objects. PostgreSQL's text search facility provides four types of configuration-related database objects:

Text search templates provide the functions underlying dictionaries. (A dictionary simply specifies a template and a set of parameters for the template.)

Text search configurations select a parser and a set of dictionaries to use to normalize the tokens produced by the parser.

Text search parsers and templates are built from low-level C functions; therefore it requires C programming ability to develop new ones, and superuser privileges to install one into a database. (There are examples of add-on parsers and templates in the contrib/ area of the PostgreSQL distribution.) Since dictionaries and configurations just parameterize and connect together some underlying parsers and templates, no special privilege is needed to create a new dictionary or configuration. Examples of creating custom dictionaries and configurations appear later in this chapter.

Tables and Indexes

The examples in the previous section illustrated full text matching using simple constant strings. This section shows how to search table data, optionally using indexes.

Searching a Table

It is possible to do a full text search without an index. A simple query to print the title of each row that contains the word friend in its body field is:

SELECT title
FROM pgweb
WHERE to_tsvector('english', body) @@ to_tsquery('english', 'friend');
This will also find related words such as friends and friendly, since all these are reduced to the same normalized lexeme.

The query above specifies that the english configuration is to be used to parse and normalize the strings. Alternatively we could omit the configuration parameters:

SELECT title
FROM pgweb
WHERE to_tsvector(body) @@ to_tsquery('friend');
This query will use the configuration set by default_text_search_config.

A more complex example is to select the ten most recent documents that contain create and table in the title or body:

SELECT title
FROM pgweb
WHERE to_tsvector(title || ' ' || body) @@ to_tsquery('create & table')
ORDER BY last_mod_date DESC
LIMIT 10;
For clarity we omitted the coalesce function calls which would be needed to find rows that contain NULL in one of the two fields.

Although these queries will work without an index, most applications will find this approach too slow, except perhaps for occasional ad-hoc searches. Practical use of text searching usually requires creating an index.

Creating Indexes

We can create a GIN index (Section 12.9) to speed up text searches:

CREATE INDEX pgweb_idx ON pgweb USING gin(to_tsvector('english', body));
Notice that the 2-argument version of to_tsvector is used. Only text search functions that specify a configuration name can be used in expression indexes (Section 11.7). This is because the index contents must be unaffected by default_text_search_config. If they were affected, the index contents might be inconsistent because different entries could contain tsvectors that were created with different text search configurations, and there would be no way to guess which was which. It would be impossible to dump and restore such an index correctly.

Because the two-argument version of to_tsvector was used in the index above, only a query reference that uses the 2-argument version of to_tsvector with the same configuration name will use that index. That is, WHERE to_tsvector('english', body) @@ 'a & b' can use the index, but WHERE to_tsvector(body) @@ 'a & b' cannot. This ensures that an index will be used only with the same configuration used to create the index entries.

It is possible to set up more complex expression indexes wherein the configuration name is specified by another column, e.g.:

CREATE INDEX pgweb_idx ON pgweb USING gin(to_tsvector(config_name, body));
where config_name is a column in the pgweb table. This allows mixed configurations in the same index while recording which configuration was used for each index entry. This would be useful, for example, if the document collection contained documents in different languages. Again, queries that are meant to use the index must be phrased to match, e.g., WHERE to_tsvector(config_name, body) @@ 'a & b'.

Indexes can even concatenate columns:

CREATE INDEX pgweb_idx ON pgweb USING gin(to_tsvector('english', title || ' ' || body));
Another approach is to create a separate tsvector column to hold the output of to_tsvector. This example is a concatenation of title and body, using coalesce to ensure that one field will still be indexed when the other is NULL:

CREATE INDEX textsearch_idx ON pgweb USING gin(textsearchable_index_col);
Now we are ready to perform a fast full text search:

SELECT title
FROM pgweb
WHERE textsearchable_index_col @@ to_tsquery('create & table')
ORDER BY last_mod_date DESC
LIMIT 10;
When using a separate column to store the tsvector representation, it is necessary to create a trigger to keep the tsvector column current anytime title or body changes. Section 12.4.3 explains how to do that.

One advantage of the separate-column approach over an expression index is that it is not necessary to explicitly specify the text search configuration in queries in order to make use of the index. As shown in the example above, the query can depend on default_text_search_config. Another advantage is that searches will be faster, since it will not be necessary to redo the to_tsvector calls to verify index matches. (This is more important when using a GiST index than a GIN index; see Section 12.9.) The expression-index approach is simpler to set up, however, and it requires less disk space since the tsvector representation is not stored explicitly.

Controlling Text Search

To implement full text searching there must be a function to create a tsvector from a document and a tsquery from a user query. Also, we need to return results in a useful order, so we need a function that compares documents with respect to their relevance to the query. It's also important to be able to display the results nicely. PostgreSQL provides support for all of these functions.

Parsing Documents

PostgreSQL provides the function to_tsvector for converting a document to the tsvector data type.

to_tsvector([ config regconfig, ] document text) returns tsvector
to_tsvector parses a textual document into tokens, reduces the tokens to lexemes, and returns a tsvector which lists the lexemes together with their positions in the document. The document is processed according to the specified or default text search configuration. Here is a simple example:

In the example above we see that the resulting tsvector does not contain the words a, on, or it, the word rats became rat, and the punctuation sign - was ignored.

The to_tsvector function internally calls a parser which breaks the document text into tokens and assigns a type to each token. For each token, a list of dictionaries (Section 12.6) is consulted, where the list can vary depending on the token type. The first dictionary that recognizes the token emits one or more normalized lexemes to represent the token. For example, rats became rat because one of the dictionaries recognized that the word rats is a plural form of rat. Some words are recognized as stop words (Section 12.6.1), which causes them to be ignored since they occur too frequently to be useful in searching. In our example these are a, on, and it. If no dictionary in the list recognizes the token then it is also ignored. In this example that happened to the punctuation sign - because there are in fact no dictionaries assigned for its token type (Space symbols), meaning space tokens will never be indexed. The choices of parser, dictionaries and which types of tokens to index are determined by the selected text search configuration (Section 12.7). It is possible to have many different configurations in the same database, and predefined configurations are available for various languages. In our example we used the default configuration english for the English language.

The function setweight can be used to label the entries of a tsvector with a given weight, where a weight is one of the letters A, B, C, or D. This is typically used to mark entries coming from different parts of a document, such as title versus body. Later, this information can be used for ranking of search results.

Because to_tsvector(NULL) will return NULL, it is recommended to use coalesce whenever a field might be null. Here is the recommended method for creating a tsvector from a structured document:

Here we have used setweight to label the source of each lexeme in the finished tsvector, and then merged the labeled tsvector values using the tsvector concatenation operator ||. (Section 12.4.1 gives details about these operations.)

Parsing Queries

PostgreSQL provides the functions to_tsquery and plainto_tsquery for converting a query to the tsquery data type. to_tsquery offers access to more features than plainto_tsquery, but is less forgiving about its input.

to_tsquery([ config regconfig, ] querytext text) returns tsquery
to_tsquery creates a tsquery value from querytext, which must consist of single tokens separated by the Boolean operators & (AND), | (OR) and ! (NOT). These operators can be grouped using parentheses. In other words, the input to to_tsquery must already follow the general rules for tsquery input, as described in Section 8.11. The difference is that while basic tsquery input takes the tokens at face value, to_tsquery normalizes each token to a lexeme using the specified or default configuration, and discards any tokens that are stop words according to the configuration. For example:

SELECT to_tsquery('english', 'The & Fat & Rats');

to_tsquery

'fat' & 'rat'

As in basic tsquery input, weight(s) can be attached to each lexeme to restrict it to match only tsvector lexemes of those weight(s). For example:

SELECT to_tsquery('english', 'Fat | Rats:AB');

to_tsquery

'fat' | 'rat':AB

Also, * can be attached to a lexeme to specify prefix matching:

SELECT to_tsquery('supern:*A & star:A*B');

to_tsquery

'supern':*A & 'star':*AB

Such a lexeme will match any word in a tsvector that begins with the given string.

to_tsquery can also accept single-quoted phrases. This is primarily useful when the configuration includes a thesaurus dictionary that may trigger on such phrases. In the example below, a thesaurus contains the rule supernovae stars : sn:

SELECT to_tsquery('supernovae stars & !crab');

to_tsquery

'sn' & !'crab'

Without quotes, to_tsquery will generate a syntax error for tokens that are not separated by an AND or OR operator.

plainto_tsquery([ config regconfig, ] querytext text) returns tsquery
plainto_tsquery transforms unformatted text querytext to tsquery. The text is parsed and normalized much as for to_tsvector, then the & (AND) Boolean operator is inserted between surviving words.

Ranking Search Results

Ranking attempts to measure how relevant documents are to a particular query, so that when there are many matches the most relevant ones can be shown first. PostgreSQL provides two predefined ranking functions, which take into account lexical, proximity, and structural information; that is, they consider how often the query terms appear in the document, how close together the terms are in the document, and how important is the part of the document where they occur. However, the concept of relevancy is vague and very application-specific. Different applications might require additional information for ranking, e.g., document modification time. The built-in ranking functions are only examples. You can write your own ranking functions and/or combine their results with additional factors to fit your specific needs.

The two ranking functions currently available are:

ts_rank([ weights float4[], ] vector tsvector,

query tsquery [, normalization integer ]) returns float4

Standard ranking function.

ts_rank_cd([ weights float4[], ] vector tsvector,

query tsquery [, normalization integer ]) returns float4

This function computes the cover density ranking for the given document vector and query, as described in Clarke, Cormack, and Tudhope's "Relevance Ranking for One to Three Term Queries" in the journal "Information Processing and Management", 1999.

This function requires positional information in its input. Therefore it will not work on "stripped" tsvector values — it will always return zero.

For both these functions, the optional weights argument offers the ability to weigh word instances more or less heavily depending on how they are labeled. The weight arrays specify how heavily to weigh each category of word, in the order:

{D-weight, C-weight, B-weight, A-weight}
If no weights are provided, then these defaults are used:

{0.1, 0.2, 0.4, 1.0}
Typically weights are used to mark words from special areas of the document, like the title or an initial abstract, so they can be treated with more or less importance than words in the document body.

Since a longer document has a greater chance of containing a query term it is reasonable to take into account document size, e.g., a hundred-word document with five instances of a search word is probably more relevant than a thousand-word document with five instances. Both ranking functions take an integer normalization option that specifies whether and how a document's length should impact its rank. The integer option controls several behaviors, so it is a bit mask: you can specify one or more behaviors using | (for example, 2|4).

0 (the default) ignores the document length

1 divides the rank by 1 + the logarithm of the document length

2 divides the rank by the document length

4 divides the rank by the mean harmonic distance between extents (this is implemented only by ts_rank_cd)

8 divides the rank by the number of unique words in document

16 divides the rank by 1 + the logarithm of the number of unique words in document

32 divides the rank by itself + 1

If more than one flag bit is specified, the transformations are applied in the order listed.

It is important to note that the ranking functions do not use any global information, so it is impossible to produce a fair normalization to 1% or 100% as sometimes desired. Normalization option 32 (rank/(rank+1)) can be applied to scale all ranks into the range zero to one, but of course this is just a cosmetic change; it will not affect the ordering of the search results.

Ranking can be expensive since it requires consulting the tsvector of each matching document, which can be I/O bound and therefore slow. Unfortunately, it is almost impossible to avoid since practical queries often result in large numbers of matches.

Highlighting Results

To present search results it is ideal to show a part of each document and how it is related to the query. Usually, search engines show fragments of the document with marked search terms. PostgreSQL provides a function ts_headline that implements this functionality.

ts_headline([ config regconfig, ] document text, query tsquery [, options text ]) returns text
ts_headline accepts a document along with a query, and returns an excerpt from the document in which terms from the query are highlighted. The configuration to be used to parse the document can be specified by config; if config is omitted, the default_text_search_config configuration is used.

If an options string is specified it must consist of a comma-separated list of one or more option=value pairs. The available options are:

StartSel, StopSel: the strings with which to delimit query words appearing in the document, to distinguish them from other excerpted words. You must double-quote these strings if they contain spaces or commas.

MaxWords, MinWords: these numbers determine the longest and shortest headlines to output.

ShortWord: words of this length or less will be dropped at the start and end of a headline. The default value of three eliminates common English articles.

HighlightAll: Boolean flag; if true the whole document will be used as the headline, ignoring the preceding three parameters.

MaxFragments: maximum number of text excerpts or fragments to display. The default value of zero selects a non-fragment-oriented headline generation method. A value greater than zero selects fragment-based headline generation. This method finds text fragments with as many query words as possible and stretches those fragments around the query words. As a result query words are close to the middle of each fragment and have words on each side. Each fragment will be of at most MaxWords and words of length ShortWord or less are dropped at the start and end of each fragment. If not all query words are found in the document, then a single fragment of the first MinWords in the document will be displayed.

FragmentDelimiter: When more than one fragment is displayed, the fragments will be separated by this string.

containing given <query> terms
and return them in order of their <similarity> to the
<query>.

ts_headline uses the original document, not a tsvector summary, so it can be slow and should be used with care. A typical mistake is to call ts_headline for every matching document when only ten documents are to be shown. SQL subqueries can help; here is an example:

FROM apod, to_tsquery('stars') q
WHERE ti @@ q
ORDER BY rank DESC
LIMIT 10) AS foo;

Additional Features

This section describes additional functions and operators that are useful in connection with text search.

Manipulating Documents

Section 12.3.1 showed how raw textual documents can be converted into tsvector values. PostgreSQL also provides functions and operators that can be used to manipulate documents that are already in tsvector form.

tsvector || tsvector
The tsvector concatenation operator returns a vector which combines the lexemes and positional information of the two vectors given as arguments. Positions and weight labels are retained during the concatenation. Positions appearing in the right-hand vector are offset by the largest position mentioned in the left-hand vector, so that the result is nearly equivalent to the result of performing to_tsvector on the concatenation of the two original document strings. (The equivalence is not exact, because any stop-words removed from the end of the left-hand argument will not affect the result, whereas they would have affected the positions of the lexemes in the right-hand argument if textual concatenation were used.)

One advantage of using concatenation in the vector form, rather than concatenating text before applying to_tsvector, is that you can use different configurations to parse different sections of the document. Also, because the setweight function marks all lexemes of the given vector the same way, it is necessary to parse the text and do setweight before concatenating if you want to label different parts of the document with different weights.

setweight(vector tsvector, weight "char") returns tsvector
setweight returns a copy of the input vector in which every position has been labeled with the given weight, either A, B, C, or D. (D is the default for new vectors and as such is not displayed on output.) These labels are retained when vectors are concatenated, allowing words from different parts of a document to be weighted differently by ranking functions.

Note that weight labels apply to positions, not lexemes. If the input vector has been stripped of positions then setweight does nothing.

length(vector tsvector) returns integer
Returns the number of lexemes stored in the vector.

strip(vector tsvector) returns tsvector
Returns a vector which lists the same lexemes as the given vector, but which lacks any position or weight information. While the returned vector is much less useful than an unstripped vector for relevance ranking, it will usually be much smaller.

Manipulating Queries

Section 12.3.2 showed how raw textual queries can be converted into tsquery values. PostgreSQL also provides functions and operators that can be used to manipulate queries that are already in tsquery form.

tsquery && tsquery
Returns the AND-combination of the two given queries.

tsquery || tsquery
Returns the OR-combination of the two given queries.

!! tsquery
Returns the negation (NOT) of the given query.

numnode(query tsquery) returns integer
Returns the number of nodes (lexemes plus operators) in a tsquery. This function is useful to determine if the query is meaningful (returns > 0), or contains only stop words (returns 0). Examples:

querytree(query tsquery) returns text
Returns the portion of a tsquery that can be used for searching an index. This function is useful for detecting unindexable queries, for example those containing only stop words or only negated terms. For example:

SELECT querytree(to_tsquery('!defined'));

querytree

Query Rewriting

The ts_rewrite family of functions search a given tsquery for occurrences of a target subquery, and replace each occurrence with a substitute subquery. In essence this operation is a tsquery-specific version of substring replacement. A target and substitute combination can be thought of as a query rewrite rule. A collection of such rewrite rules can be a powerful search aid. For example, you can expand the search using synonyms (e.g., new york, big apple, nyc, gotham) or narrow the search to direct the user to some hot topic. There is some overlap in functionality between this feature and thesaurus dictionaries (Section 12.6.4). However, you can modify a set of rewrite rules on-the-fly without reindexing, whereas updating a thesaurus requires reindexing to be effective.

ts_rewrite (query tsquery, target tsquery, substitute tsquery) returns tsquery
This form of ts_rewrite simply applies a single rewrite rule: target is replaced by substitute wherever it appears in query. For example:

SELECT ts_rewrite('a & b'::tsquery, 'a'::tsquery, 'c'::tsquery);

ts_rewrite

'b' & 'c'

ts_rewrite (query tsquery, select text) returns tsquery
This form of ts_rewrite accepts a starting query and a SQL select command, which is given as a text string. The select must yield two columns of tsquery type. For each row of the select result, occurrences of the first column value (the target) are replaced by the second column value (the substitute) within the current query value. For example:

Rewriting can be slow when there are many rewriting rules, since it checks every rule for a possible match. To filter out obvious non-candidate rules we can use the containment operators for the tsquery type. In the example below, we select only those rules which might match the original query:

SELECT ts_rewrite('a & b'::tsquery,

'SELECT t,s FROM aliases WHERE a & b::tsquery @> t');
ts_rewrite

'b' & 'c'

Triggers for Automatic Updates

When using a separate column to store the tsvector representation of your documents, it is necessary to create a trigger to update the tsvector column when the document content columns change. Two built-in trigger functions are available for this, or you can write your own.

tsvector_update_trigger(tsvector_column_name, config_name, text_column_name [, ... ])
tsvector_update_trigger_column(tsvector_column_name, config_column_name, text_column_name [, ... ])
These trigger functions automatically compute a tsvector column from one or more textual columns, under the control of parameters specified in the CREATE TRIGGER command. An example of their use is:

Having created this trigger, any change in title or body will automatically be reflected into tsv, without the application having to worry about it.

The first trigger argument must be the name of the tsvector column to be updated. The second argument specifies the text search configuration to be used to perform the conversion. For tsvector_update_trigger, the configuration name is simply given as the second trigger argument. It must be schema-qualified as shown above, so that the trigger behavior will not change with changes in search_path. For tsvector_update_trigger_column, the second trigger argument is the name of another table column, which must be of type regconfig. This allows a per-row selection of configuration to be made. The remaining argument(s) are the names of textual columns (of type text, varchar, or char). These will be included in the document in the order given. NULL values will be skipped (but the other columns will still be indexed).

A limitation of these built-in triggers is that they treat all the input columns alike. To process columns differently — for example, to weight title differently from body — it is necessary to write a custom trigger. Here is an example using PL/pgSQL as the trigger language:

Keep in mind that it is important to specify the configuration name explicitly when creating tsvector values inside triggers, so that the column's contents will not be affected by changes to default_text_search_config. Failure to do this is likely to lead to problems such as search results changing after a dump and reload.

Gathering Document Statistics

The function ts_stat is useful for checking your configuration and for finding stop-word candidates.

ts_stat(sqlquery text, [ weights text, ]

OUT word text, OUT ndoc integer,
OUT nentry integer) returns setof record

sqlquery is a text value containing an SQL query which must return a single tsvector column. ts_stat executes the query and returns statistics about each distinct lexeme (word) contained in the tsvector data. The columns returned are

word text — the value of a lexeme

ndoc integer — number of documents (tsvectors) the word occurred in

nentry integer — total number of occurrences of the word

If weights is supplied, only occurrences having one of those weights are counted.

For example, to find the ten most frequent words in a document collection:

SELECT * FROM ts_stat('SELECT vector FROM apod')
ORDER BY nentry DESC, ndoc DESC, word
LIMIT 10;
The same, but counting only word occurrences with weight A or B:

Parsers

Text search parsers are responsible for splitting raw document text into tokens and identifying each token's type, where the set of possible types is defined by the parser itself. Note that a parser does not modify the text at all — it simply identifies plausible word boundaries. Because of this limited scope, there is less need for application-specific custom parsers than there is for custom dictionaries. At present PostgreSQL provides just one built-in parser, which has been found to be useful for a wide range of applications.

The built-in parser is named pg_catalog.default. It recognizes 23 token types:

Table 12-1. Default Parser's Token Types

Alias

Description

Example

asciiword

Word, all ASCII letters

elephant

word

Word, all letters

mañana

numword

Word, letters and digits

beta1

asciihword

Hyphenated word, all ASCII

up-to-date

hword

Hyphenated word, all letters

lógico-matemática

numhword

Hyphenated word, letters and digits

postgresql-beta1

hword_asciipart

Hyphenated word part, all ASCII

postgresql in the context postgresql-beta1

hword_part

Hyphenated word part, all letters

lógico or matemática in the context lógico-matemática

hword_numpart

Hyphenated word part, letters and digits

beta1 in the context postgresql-beta1

email

Email address

foo@example.com

protocol

Protocol head

http://

url

URL

example.com/stuff/index.html

host

Host

example.com

url_path

URL path

/stuff/index.html, in the context of a URL

file

File or path name

/usr/local/foo.txt, if not within a URL

sfloat

Scientific notation

-1.234e56

float

Decimal notation

-1.234

int

Signed integer

-1234

uint

Unsigned integer

1234

version

Version number

8.3.0

tag

XML tag

<a href="dictionaries.html">

entity

XML entity

&

blank

Space symbols

(any whitespace or punctuation not otherwise recognized)

Note: The parser's notion of a "letter" is determined by the database's locale setting, specifically lc_ctype. Words containing only the basic ASCII letters are reported as a separate token type, since it is sometimes useful to distinguish them. In most European languages, token types word and asciiword should be treated alike.

email does not support all valid email characters as defined by RFC 5322. Specifically, the only non-alphanumeric characters supported for email user names are period, dash, and underscore.

It is possible for the parser to produce overlapping tokens from the same piece of text. As an example, a hyphenated word will be reported both as the entire word and as each component:

Dictionaries

Dictionaries are used to eliminate words that should not be considered in a search (stop words), and to normalize words so that different derived forms of the same word will match. A successfully normalized word is called a lexeme. Aside from improving search quality, normalization and removal of stop words reduce the size of the tsvector representation of a document, thereby improving performance. Normalization does not always have linguistic meaning and usually depends on application semantics.

If indexing numbers, we can remove some fractional digits to reduce the range of possible numbers, so for example 3.14159265359, 3.1415926, 3.14 will be the same after normalization if only two digits are kept after the decimal point.

A dictionary is a program that accepts a token as input and returns:

an array of lexemes if the input token is known to the dictionary (notice that one token can produce more than one lexeme)

a single lexeme with the TSL_FILTER flag set, to replace the original token with a new token to be passed to subsequent dictionaries (a dictionary that does this is called a filtering dictionary)

an empty array if the dictionary knows the token, but it is a stop word

NULL if the dictionary does not recognize the input token

PostgreSQL provides predefined dictionaries for many languages. There are also several predefined templates that can be used to create new dictionaries with custom parameters. Each predefined dictionary template is described below. If no existing template is suitable, it is possible to create new ones; see the contrib/ area of the PostgreSQL distribution for examples.

A text search configuration binds a parser together with a set of dictionaries to process the parser's output tokens. For each token type that the parser can return, a separate list of dictionaries is specified by the configuration. When a token of that type is found by the parser, each dictionary in the list is consulted in turn, until some dictionary recognizes it as a known word. If it is identified as a stop word, or if no dictionary recognizes the token, it will be discarded and not indexed or searched for. Normally, the first dictionary that returns a non-NULL output determines the result, and any remaining dictionaries are not consulted; but a filtering dictionary can replace the given word with a modified word, which is then passed to subsequent dictionaries.

The general rule for configuring a list of dictionaries is to place first the most narrow, most specific dictionary, then the more general dictionaries, finishing with a very general dictionary, like a Snowball stemmer or simple, which recognizes everything. For example, for an astronomy-specific search (astro_en configuration) one could bind token type asciiword (ASCII word) to a synonym dictionary of astronomical terms, a general English dictionary and a Snowball English stemmer:

A filtering dictionary can be placed anywhere in the list, except at the end where it'd be useless. Filtering dictionaries are useful to partially normalize words to simplify the task of later dictionaries. For example, a filtering dictionary could be used to remove accents from accented letters, as is done by the Section F.44 module.

Stop Words

Stop words are words that are very common, appear in almost every document, and have no discrimination value. Therefore, they can be ignored in the context of full text searching. For example, every English text contains words like a and the, so it is useless to store them in an index. However, stop words do affect the positions in tsvector, which in turn affect ranking:

SELECT to_tsvector('english','in the list of stop words');

to_tsvector

'list':3 'stop':5 'word':6

The missing positions 1,2,4 are because of stop words. Ranks calculated for documents with and without stop words are quite different:

It is up to the specific dictionary how it treats stop words. For example, ispell dictionaries first normalize words and then look at the list of stop words, while Snowball stemmers first check the list of stop words. The reason for the different behavior is an attempt to decrease noise.

Simple Dictionary

The simple dictionary template operates by converting the input token to lower case and checking it against a file of stop words. If it is found in the file then an empty array is returned, causing the token to be discarded. If not, the lower-cased form of the word is returned as the normalized lexeme. Alternatively, the dictionary can be configured to report non-stop-words as unrecognized, allowing them to be passed on to the next dictionary in the list.

Here is an example of a dictionary definition using the simple template:

CREATE TEXT SEARCH DICTIONARY public.simple_dict (

TEMPLATE = pg_catalog.simple,
STOPWORDS = english

);
Here, english is the base name of a file of stop words. The file's full name will be $SHAREDIR/tsearch_data/english.stop, where $SHAREDIR means the PostgreSQL installation's shared-data directory, often /usr/local/share/postgresql (use pg_config --sharedir to determine it if you're not sure). The file format is simply a list of words, one per line. Blank lines and trailing spaces are ignored, and upper case is folded to lower case, but no other processing is done on the file contents.

Now we can test our dictionary:

SELECT ts_lexize('public.simple_dict','YeS');

ts_lexize

{yes}

SELECT ts_lexize('public.simple_dict','The');

ts_lexize

{}

We can also choose to return NULL, instead of the lower-cased word, if it is not found in the stop words file. This behavior is selected by setting the dictionary's Accept parameter to false. Continuing the example:

ALTER TEXT SEARCH DICTIONARY public.simple_dict ( Accept = false );

SELECT ts_lexize('public.simple_dict','YeS');

ts_lexize

SELECT ts_lexize('public.simple_dict','The');

ts_lexize

{}

With the default setting of Accept = true, it is only useful to place a simple dictionary at the end of a list of dictionaries, since it will never pass on any token to a following dictionary. Conversely, Accept = false is only useful when there is at least one following dictionary.

Caution
Most types of dictionaries rely on configuration files, such as files of stop words. These files must be stored in UTF-8 encoding. They will be translated to the actual database encoding, if that is different, when they are read into the server.

Caution
Normally, a database session will read a dictionary configuration file only once, when it is first used within the session. If you modify a configuration file and want to force existing sessions to pick up the new contents, issue an ALTER TEXT SEARCH DICTIONARY command on the dictionary. This can be a "dummy" update that doesn't actually change any parameter values.

Synonym Dictionary

This dictionary template is used to create dictionaries that replace a word with a synonym. Phrases are not supported (use the thesaurus template (Section 12.6.4) for that). A synonym dictionary can be used to overcome linguistic problems, for example, to prevent an English stemmer dictionary from reducing the word 'Paris' to 'pari'. It is enough to have a Paris paris line in the synonym dictionary and put it before the english_stem dictionary. For example:

The only parameter required by the synonym template is SYNONYMS, which is the base name of its configuration file — my_synonyms in the above example. The file's full name will be $SHAREDIR/tsearch_data/my_synonyms.syn (where $SHAREDIR means the PostgreSQL installation's shared-data directory). The file format is just one line per word to be substituted, with the word followed by its synonym, separated by white space. Blank lines and trailing spaces are ignored.

The synonym template also has an optional parameter CaseSensitive, which defaults to false. When CaseSensitive is false, words in the synonym file are folded to lower case, as are input tokens. When it is true, words and tokens are not folded to lower case, but are compared as-is.

An asterisk (*) can be placed at the end of a synonym in the configuration file. This indicates that the synonym is a prefix. The asterisk is ignored when the entry is used in to_tsvector(), but when it is used in to_tsquery(), the result will be a query item with the prefix match marker (see Section 12.3.2). For example, suppose we have these entries in $SHAREDIR/tsearch_data/synonym_sample.syn:

postgres pgsql
postgresql pgsql
postgre pgsql
gogle googl
indices index*
Then we will get these results:

Thesaurus Dictionary

A thesaurus dictionary (sometimes abbreviated as TZ) is a collection of words that includes information about the relationships of words and phrases, i.e., broader terms (BT), narrower terms (NT), preferred terms, non-preferred terms, related terms, etc.

Basically a thesaurus dictionary replaces all non-preferred terms by one preferred term and, optionally, preserves the original terms for indexing as well. PostgreSQL's current implementation of the thesaurus dictionary is an extension of the synonym dictionary with added phrase support. A thesaurus dictionary requires a configuration file of the following format:

this is a comment

sample word(s) : indexed word(s)
more sample word(s) : more indexed word(s)
...
where the colon (:) symbol acts as a delimiter between a a phrase and its replacement.

A thesaurus dictionary uses a subdictionary (which is specified in the dictionary's configuration) to normalize the input text before checking for phrase matches. It is only possible to select one subdictionary. An error is reported if the subdictionary fails to recognize a word. In that case, you should remove the use of the word or teach the subdictionary about it. You can place an asterisk (*) at the beginning of an indexed word to skip applying the subdictionary to it, but all sample words must be known to the subdictionary.

The thesaurus dictionary chooses the longest match if there are multiple phrases matching the input, and ties are broken by using the last definition.

Specific stop words recognized by the subdictionary cannot be specified; instead use ? to mark the location where any stop word can appear. For example, assuming that a and the are stop words according to the subdictionary:

? one ? two : swsw
matches a one the two and the one a two; both would be replaced by swsw.

Since a thesaurus dictionary has the capability to recognize phrases it must remember its state and interact with the parser. A thesaurus dictionary uses these assignments to check if it should handle the next word or stop accumulation. The thesaurus dictionary must be configured carefully. For example, if the thesaurus dictionary is assigned to handle only the asciiword token, then a thesaurus dictionary definition like one 7 will not work since token type uint is not assigned to the thesaurus dictionary.

Caution
Thesauruses are used during indexing so any change in the thesaurus dictionary's parameters requires reindexing. For most other dictionary types, small changes such as adding or removing stopwords does not force reindexing.

Thesaurus Configuration

To define a new thesaurus dictionary, use the thesaurus template. For example:

mythesaurus is the base name of the thesaurus configuration file. (Its full name will be $SHAREDIR/tsearch_data/mythesaurus.ths, where $SHAREDIR means the installation shared-data directory.)

pg_catalog.english_stem is the subdictionary (here, a Snowball English stemmer) to use for thesaurus normalization. Notice that the subdictionary will have its own configuration (for example, stop words), which is not shown here.

Now it is possible to bind the thesaurus dictionary thesaurus_simple to the desired token types in a configuration, for example:

Now we can see how it works. ts_lexize is not very useful for testing a thesaurus, because it treats its input as a single token. Instead we can use plainto_tsquery and to_tsvector which will break their input strings into multiple tokens:

SELECT plainto_tsquery('supernova star');

plainto_tsquery

'sn'

SELECT to_tsvector('supernova star');

to_tsvector

'sn':1

In principle, one can use to_tsquery if you quote the argument:

SELECT to_tsquery(supernova star);

to_tsquery

'sn'

Notice that supernova star matches supernovae stars in thesaurus_astro because we specified the english_stem stemmer in the thesaurus definition. The stemmer removed the e and s.

To index the original phrase as well as the substitute, just include it in the right-hand part of the definition:

supernovae stars : sn supernovae stars

SELECT plainto_tsquery('supernova star');

plainto_tsquery

'sn' & 'supernova' & 'star'

Ispell Dictionary

The Ispell dictionary template supports morphological dictionaries, which can normalize many different linguistic forms of a word into the same lexeme. For example, an English Ispell dictionary can match all declensions and conjugations of the search term bank, e.g., banking, banked, banks, banks', and bank's.

The standard PostgreSQL distribution does not include any Ispell configuration files. Dictionaries for a large number of languages are available from Ispell. Also, some more modern dictionary file formats are supported — MySpell (OO < 2.0.1) and Hunspell (OO >= 2.0.2). A large list of dictionaries is available on the OpenOffice Wiki.

To create an Ispell dictionary, use the built-in ispell template and specify several parameters:

);
Here, DictFile, AffFile, and StopWords specify the base names of the dictionary, affixes, and stop-words files. The stop-words file has the same format explained above for the simple dictionary type. The format of the other files is not specified here but is available from the above-mentioned web sites.

Ispell dictionaries usually recognize a limited set of words, so they should be followed by another broader dictionary; for example, a Snowball dictionary, which recognizes everything.

Ispell dictionaries support splitting compound words; a useful feature. Notice that the affix file should specify a special flag using the compoundwords controlled statement that marks dictionary words that can participate in compound formation:

compoundwords controlled z
Here are some examples for the Norwegian language:

Note: MySpell does not support compound words. Hunspell has sophisticated support for compound words. At present, PostgreSQL implements only the basic compound word operations of Hunspell.

Snowball Dictionary

The Snowball dictionary template is based on a project by Martin Porter, inventor of the popular Porter's stemming algorithm for the English language. Snowball now provides stemming algorithms for many languages (see the Snowball site for more information). Each algorithm understands how to reduce common variant forms of words to a base, or stem, spelling within its language. A Snowball dictionary requires a language parameter to identify which stemmer to use, and optionally can specify a stopword file name that gives a list of words to eliminate. (PostgreSQL's standard stopword lists are also provided by the Snowball project.) For example, there is a built-in definition equivalent to

CREATE TEXT SEARCH DICTIONARY english_stem (

TEMPLATE = snowball,
Language = english,
StopWords = english

);
The stopword file format is the same as already explained.

A Snowball dictionary recognizes everything, whether or not it is able to simplify the word, so it should be placed at the end of the dictionary list. It is useless to have it before any other dictionary because a token will never pass through it to the next dictionary.

Configuration Example

A text search configuration specifies all options necessary to transform a document into a tsvector: the parser to use to break text into tokens, and the dictionaries to use to transform each token into a lexeme. Every call of to_tsvector or to_tsquery needs a text search configuration to perform its processing. The configuration parameter default_text_search_config specifies the name of the default configuration, which is the one used by text search functions if an explicit configuration parameter is omitted. It can be set in postgresql.conf, or set for an individual session using the SET command.

Several predefined text search configurations are available, and you can create custom configurations easily. To facilitate management of text search objects, a set of SQL commands is available, and there are several psql commands that display information about text search objects (Section 12.10).

As an example we will create a configuration pg, starting by duplicating the built-in english configuration:

We choose not to index or search some token types that the built-in configuration does handle:

ALTER TEXT SEARCH CONFIGURATION pg

DROP MAPPING FOR email, url, url_path, sfloat, float;

Now we can test our configuration:

SELECT * FROM ts_debug('public.pg', '
PostgreSQL, the highly scalable, SQL compliant, open source object-relational
database management system, is now undergoing beta testing of the next
version of our software.
');
The next step is to set the session to use the new configuration, which was created in the public schema:

=> \dF

List of text search configurations
Schema | Name | Description

+------+-------------

public | pg |

SET default_text_search_config = 'public.pg';
SET

SHOW default_text_search_config;

default_text_search_config

public.pg

Testing and Debugging Text Search

The behavior of a custom text search configuration can easily become confusing. The functions described in this section are useful for testing text search objects. You can test a complete configuration, or test parsers and dictionaries separately.

Configuration Testing

OUT alias text,
OUT description text,
OUT token text,
OUT dictionaries regdictionary[],
OUT dictionary regdictionary,
OUT lexemes text[])
returns setof record

ts_debug displays information about every token of document as produced by the parser and processed by the configured dictionaries. It uses the configuration specified by config, or default_text_search_config if that argument is omitted.

ts_debug returns one row for each token identified in the text by the parser. The columns returned are

alias text — short name of the token type

description text — description of the token type

token text — text of the token

dictionaries regdictionary[] — the dictionaries selected by the configuration for this token type

dictionary regdictionary — the dictionary that recognized the token, or NULL if none did

lexemes text[] — the lexeme(s) produced by the dictionary that recognized the token, or NULL if none did; an empty array ({}) means it was recognized as a stop word

Here is a simple example:

SELECT * FROM ts_debug('english','a fat cat sat on a mat - it ate a fat rats');

In this example, the word Brightest was recognized by the parser as an ASCII word (alias asciiword). For this token type the dictionary list is english_ispell and english_stem. The word was recognized by english_ispell, which reduced it to the noun bright. The word supernovaes is unknown to the english_ispell dictionary so it was passed to the next dictionary, and, fortunately, was recognized (in fact, english_stem is a Snowball dictionary which recognizes everything; that is why it was placed at the end of the dictionary list).

The word The was recognized by the english_ispell dictionary as a stop word (Section 12.6.1) and will not be indexed. The spaces are discarded too, since the configuration provides no dictionaries at all for them.

You can reduce the width of the output by explicitly specifying which columns you want to see:

ts_parse parses the given document and returns a series of records, one for each token produced by parsing. Each record includes a tokid showing the assigned token type and a token which is the text of the token. For example:

SELECT * FROM ts_parse('default', '123 - a number');

tokid | token

+--------

22 | 123
12 |
12 | -
1 | a
12 |
1 | number

ts_token_type(parser_name text, OUT tokid integer,

OUT alias text, OUT description text) returns setof record

ts_token_type(parser_oid oid, OUT tokid integer,

OUT alias text, OUT description text) returns setof record

ts_token_type returns a table which describes each type of token the specified parser can recognize. For each token type, the table gives the integer tokid that the parser uses to label a token of that type, the alias that names the token type in configuration commands, and a short description. For example:

Dictionary Testing

The ts_lexize function facilitates dictionary testing.

ts_lexize(dict regdictionary, token text) returns text[]
ts_lexize returns an array of lexemes if the input token is known to the dictionary, or an empty array if the token is known to the dictionary but it is a stop word, or NULL if it is an unknown word.

Examples:

SELECT ts_lexize('english_stem', 'stars');

ts_lexize

{star}

SELECT ts_lexize('english_stem', 'a');

ts_lexize

{}

Note: The ts_lexize function expects a single token, not text. Here is a case where this can be confusing:

SELECT ts_lexize('thesaurus_astro','supernovae stars') is null;

?column?

t

The thesaurus dictionary thesaurus_astro does know the phrase supernovae stars, but ts_lexize fails since it does not parse the input text but treats it as a single token. Use plainto_tsquery or to_tsvector to test thesaurus dictionaries, for example:

SELECT plainto_tsquery('supernovae stars');

plainto_tsquery

'sn'

GiST and GIN Index Types

There are two kinds of indexes that can be used to speed up full text searches. Note that indexes are not mandatory for full text searching, but in cases where a column is searched on a regular basis, an index is usually desirable.

CREATE INDEX name ON table USING gist(column);
Creates a GiST (Generalized Search Tree)-based index. The column can be of tsvector or tsquery type.

CREATE INDEX name ON table USING gin(column);
Creates a GIN (Generalized Inverted Index)-based index. The column must be of tsvector type.